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DIRECT MEDICAL IMAGE TO SIMULATION USING AUTO-SEGMENTATION AND POINT CLOUD-BASED CFD.

Ashton M Corpuz¹, Monu Jaiswal¹, Pan Du²

¹Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA.

Advances in Computational Science and Engineering

|February 25, 2026

Summary

This summary is machine-generated.

This study introduces a new computational method for cardiovascular digital twins. It speeds up simulations from patient scans, making complex cardiovascular disease analysis more accessible.

Keywords:

CFD CT scan Cardiovascular disease Primary: 76M10, 68T07, 92C50 Secondary: 76D05, 65N30, 68U10 autosegmentation neural network point cloud

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Area of Science:

Computational medicine
Biomedical engineering
Medical imaging

Background:

Cardiovascular disease (CVD) is a major global health concern.
Current image-based computational methods for CVD analysis face challenges in automation and require specialized expertise.
Digital twins offer potential for mitigating CVD impact but require efficient simulation pipelines.

Purpose of the Study:

To present a novel framework for accelerating computational fluid dynamics (CFD) simulations for cardiovascular digital twins.
To overcome limitations of manual segmentation and domain expertise in current image-to-simulation workflows.
To enable rapid, direct CFD simulations from patient medical images.

Main Methods:

Developed a framework using immersogeometric analysis for direct point cloud-to-CFD simulations.
Integrated advanced auto-segmentation techniques to extract patient-specific geometries as point clouds.
Validated the framework with benchmark flow problems and applied it to patient-specific cardiovascular images.

Main Results:

Demonstrated the ability to perform rapid CFD simulations directly on point clouds derived from medical scans.
Successfully applied the framework to patient-specific cardiovascular imaging data.
Showcased accelerated image-to-simulation pipeline for cardiovascular digital twins.

Conclusions:

The proposed framework significantly accelerates the process of creating cardiovascular digital twins.
This approach enhances the tractability and accessibility of advanced CVD computational analysis.
The method holds promise for improving patient-specific cardiovascular disease assessment and treatment planning.